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Commit 701e5032 authored by zhangwenwei's avatar zhangwenwei
Browse files

Merge branch 'feat_pointnet_ops' into 'master' 

Feat pointnet ops

See merge request open-mmlab/mmdet.3d!34
parents 28e511cd 006b7ccf
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mmdet3d/ops/interpolate/src/three_interpolate_cuda.cu 0 → 100644
View file @ 701e5032
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#define THREADS_PER_BLOCK 256
#define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0))
__global__ void three_interpolate_kernel(int b, int c, int m, int n, const float *__restrict__ points,
const int *__restrict__ idx, const float *__restrict__ weight, float *__restrict__ out) {
// points: (B, C, M)
// idx: (B, N, 3)
// weight: (B, N, 3)
// output:
// out: (B, C, N)
int bs_idx = blockIdx.z;
int c_idx = blockIdx.y;
int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
if (bs_idx >= b || c_idx >= c || pt_idx >= n) return;
weight += bs_idx * n * 3 + pt_idx * 3;
points += bs_idx * c * m + c_idx * m;
idx += bs_idx * n * 3 + pt_idx * 3;
out += bs_idx * c * n + c_idx * n;
out[pt_idx] = weight[0] * points[idx[0]] + weight[1] * points[idx[1]] + weight[2] * points[idx[2]];
}
void three_interpolate_kernel_launcher(int b, int c, int m, int n,
const float *points, const int *idx, const float *weight, float *out, cudaStream_t stream) {
// points: (B, C, M)
// idx: (B, N, 3)
// weight: (B, N, 3)
// output:
// out: (B, C, N)
cudaError_t err;
dim3 blocks(DIVUP(n, THREADS_PER_BLOCK), c, b); // blockIdx.x(col), blockIdx.y(row)
dim3 threads(THREADS_PER_BLOCK);
three_interpolate_kernel<<<blocks, threads, 0, stream>>>(b, c, m, n, points, idx, weight, out);
err = cudaGetLastError();
if (cudaSuccess != err) {
fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
exit(-1);
}
}
__global__ void three_interpolate_grad_kernel(int b, int c, int n, int m, const float *__restrict__ grad_out,
const int *__restrict__ idx, const float *__restrict__ weight, float *__restrict__ grad_points) {
// grad_out: (B, C, N)
// weight: (B, N, 3)
// output:
// grad_points: (B, C, M)
int bs_idx = blockIdx.z;
int c_idx = blockIdx.y;
int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
if (bs_idx >= b || c_idx >= c || pt_idx >= n) return;
grad_out += bs_idx * c * n + c_idx * n + pt_idx;
weight += bs_idx * n * 3 + pt_idx * 3;
grad_points += bs_idx * c * m + c_idx * m;
idx += bs_idx * n * 3 + pt_idx * 3;
atomicAdd(grad_points + idx[0], grad_out[0] * weight[0]);
atomicAdd(grad_points + idx[1], grad_out[0] * weight[1]);
atomicAdd(grad_points + idx[2], grad_out[0] * weight[2]);
}
void three_interpolate_grad_kernel_launcher(int b, int c, int n, int m, const float *grad_out,
const int *idx, const float *weight, float *grad_points, cudaStream_t stream) {
// grad_out: (B, C, N)
// weight: (B, N, 3)
// output:
// grad_points: (B, C, M)
cudaError_t err;
dim3 blocks(DIVUP(n, THREADS_PER_BLOCK), c, b); // blockIdx.x(col), blockIdx.y(row)
dim3 threads(THREADS_PER_BLOCK);
three_interpolate_grad_kernel<<<blocks, threads, 0, stream>>>(b, c, n, m, grad_out, idx, weight, grad_points);
err = cudaGetLastError();
if (cudaSuccess != err) {
fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
exit(-1);
}
}
mmdet3d/ops/interpolate/src/three_nn_cuda.cu 0 → 100644
View file @ 701e5032
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#define THREADS_PER_BLOCK 256
#define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0))
__global__ void three_nn_kernel(int b, int n, int m, const float *__restrict__ unknown,
const float *__restrict__ known, float *__restrict__ dist2, int *__restrict__ idx) {
// unknown: (B, N, 3)
// known: (B, M, 3)
// output:
// dist2: (B, N, 3)
// idx: (B, N, 3)
int bs_idx = blockIdx.y;
int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
if (bs_idx >= b || pt_idx >= n) return;
unknown += bs_idx * n * 3 + pt_idx * 3;
known += bs_idx * m * 3;
dist2 += bs_idx * n * 3 + pt_idx * 3;
idx += bs_idx * n * 3 + pt_idx * 3;
float ux = unknown[0];
float uy = unknown[1];
float uz = unknown[2];
double best1 = 1e40, best2 = 1e40, best3 = 1e40;
int besti1 = 0, besti2 = 0, besti3 = 0;
for (int k = 0; k < m; ++k) {
float x = known[k * 3 + 0];
float y = known[k * 3 + 1];
float z = known[k * 3 + 2];
float d = (ux - x) * (ux - x) + (uy - y) * (uy - y) + (uz - z) * (uz - z);
if (d < best1) {
best3 = best2; besti3 = besti2;
best2 = best1; besti2 = besti1;
best1 = d; besti1 = k;
}
else if (d < best2) {
best3 = best2; besti3 = besti2;
best2 = d; besti2 = k;
}
else if (d < best3) {
best3 = d; besti3 = k;
}
}
dist2[0] = best1; dist2[1] = best2; dist2[2] = best3;
idx[0] = besti1; idx[1] = besti2; idx[2] = besti3;
}
void three_nn_kernel_launcher(int b, int n, int m, const float *unknown,
const float *known, float *dist2, int *idx, cudaStream_t stream) {
// unknown: (B, N, 3)
// known: (B, M, 3)
// output:
// dist2: (B, N, 3)
// idx: (B, N, 3)
cudaError_t err;
dim3 blocks(DIVUP(n, THREADS_PER_BLOCK), b); // blockIdx.x(col), blockIdx.y(row)
dim3 threads(THREADS_PER_BLOCK);
three_nn_kernel<<<blocks, threads, 0, stream>>>(b, n, m, unknown, known, dist2, idx);
err = cudaGetLastError();
if (cudaSuccess != err) {
fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
exit(-1);
}
}
mmdet3d/ops/interpolate/three_interpolate.py 0 → 100644
View file @ 701e5032
from typing import Tuple
import torch
from torch.autograd import Function
from . import interpolate_ext
class ThreeInterpolate(Function):
@staticmethod
def forward(ctx, features: torch.Tensor, indices: torch.Tensor,
weight: torch.Tensor) -> torch.Tensor:
"""Performs weighted linear interpolation on 3 features
Args:
features (Tensor): (B, C, M) Features descriptors to be
interpolated from
indices (Tensor): (B, n, 3) index three nearest neighbors
of the target features in features
weight (Tensor): (B, n, 3) weights of interpolation
Returns:
Tensor: (B, C, N) tensor of the interpolated features
"""
assert features.is_contiguous()
assert indices.is_contiguous()
assert weight.is_contiguous()
B, c, m = features.size()
n = indices.size(1)
ctx.three_interpolate_for_backward = (indices, weight, m)
output = torch.cuda.FloatTensor(B, c, n)
interpolate_ext.three_interpolate_wrapper(B, c, m, n, features,
indices, weight, output)
return output
@staticmethod
def backward(
ctx, grad_out: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""Backward of three interpolate
Args:
grad_out (Tensor): (B, C, N) tensor with gradients of outputs
Returns:
Tensor: (B, C, M) tensor with gradients of features
"""
idx, weight, m = ctx.three_interpolate_for_backward
B, c, n = grad_out.size()
grad_features = torch.cuda.FloatTensor(B, c, m).zero_()
grad_out_data = grad_out.data.contiguous()
interpolate_ext.three_interpolate_grad_wrapper(B, c, n, m,
grad_out_data, idx,
weight,
grad_features.data)
return grad_features, None, None
three_interpolate = ThreeInterpolate.apply
mmdet3d/ops/interpolate/three_nn.py 0 → 100644
View file @ 701e5032
from typing import Tuple
import torch
from torch.autograd import Function
from . import interpolate_ext
class ThreeNN(Function):
@staticmethod
def forward(ctx, target: torch.Tensor,
source: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
"""Find the top-3 nearest neighbors of the target set from the source set.
Args:
target (Tensor): shape (B, N, 3), points set that needs to
find the nearest neighbors.
source (Tensor): shape (B, M, 3), points set that is used
to find the nearest neighbors of points in target set.
Returns:
Tensor: shape (B, N, 3), L2 distance of each point in target
set to their corresponding nearest neighbors.
"""
assert target.is_contiguous()
assert source.is_contiguous()
B, N, _ = target.size()
m = source.size(1)
dist2 = torch.cuda.FloatTensor(B, N, 3)
idx = torch.cuda.IntTensor(B, N, 3)
interpolate_ext.three_nn_wrapper(B, N, m, target, source, dist2, idx)
return torch.sqrt(dist2), idx
@staticmethod
def backward(ctx, a=None, b=None):
return None, None
three_nn = ThreeNN.apply
setup.py
View file @ 701e5032
......@@ -269,6 +269,33 @@ if __name__ == '__main__':
'src/roiaware_pool3d_kernel.cu',
'src/points_in_boxes_cuda.cu',
]),
make_cuda_ext(
name='ball_query_ext',
module='mmdet3d.ops.ball_query',
sources=['src/ball_query.cpp'],
sources_cuda=['src/ball_query_cuda.cu']),
make_cuda_ext(
name='group_points_ext',
module='mmdet3d.ops.group_points',
sources=['src/group_points.cpp'],
sources_cuda=['src/group_points_cuda.cu']),
make_cuda_ext(
name='interpolate_ext',
module='mmdet3d.ops.interpolate',
sources=['src/interpolate.cpp'],
sources_cuda=[
'src/three_interpolate_cuda.cu', 'src/three_nn_cuda.cu'
]),
make_cuda_ext(
name='furthest_point_sample_ext',
module='mmdet3d.ops.furthest_point_sample',
sources=['src/furthest_point_sample.cpp'],
sources_cuda=['src/furthest_point_sample_cuda.cu']),
make_cuda_ext(
name='gather_points_ext',
module='mmdet3d.ops.gather_points',
sources=['src/gather_points.cpp'],
sources_cuda=['src/gather_points_cuda.cu'])
],
cmdclass={'build_ext': BuildExtension},
zip_safe=False)
tests/test_pointnet_ops.py 0 → 100644
View file @ 701e5032
import pytest
import torch
from mmdet3d.ops import (ball_query, furthest_point_sample, gather_points,
grouping_operation, three_interpolate, three_nn)
def test_fps():
if not torch.cuda.is_available():
pytest.skip()
xyz = torch.tensor([[[-0.2748, 1.0020, -1.1674], [0.1015, 1.3952, -1.2681],
[-0.8070, 2.4137,
-0.5845], [-1.0001, 2.1982, -0.5859],
[0.3841, 1.8983, -0.7431]],
[[-1.0696, 3.0758,
-0.1899], [-0.2559, 3.5521, -0.1402],
[0.8164, 4.0081, -0.1839], [-1.1000, 3.0213, -0.8205],
[-0.0518, 3.7251, -0.3950]]]).cuda()
idx = furthest_point_sample(xyz, 3)
expected_idx = torch.tensor([[0, 2, 4], [0, 2, 1]]).cuda()
assert torch.all(idx == expected_idx)
def test_ball_query():
if not torch.cuda.is_available():
pytest.skip()
new_xyz = torch.tensor([[[-0.0740, 1.3147, -1.3625],
[-2.2769, 2.7817, -0.2334],
[-0.4003, 2.4666, -0.5116],
[-0.0740, 1.3147, -1.3625],
[-0.0740, 1.3147, -1.3625]],
[[-2.0289, 2.4952, -0.1708],
[-2.0668, 6.0278, -0.4875],
[0.4066, 1.4211, -0.2947],
[-2.0289, 2.4952, -0.1708],
[-2.0289, 2.4952, -0.1708]]]).cuda()
xyz = torch.tensor([[[-0.0740, 1.3147, -1.3625], [0.5555, 1.0399, -1.3634],
[-0.4003, 2.4666,
-0.5116], [-0.5251, 2.4379, -0.8466],
[-0.9691, 1.1418,
-1.3733], [-0.2232, 0.9561, -1.3626],
[-2.2769, 2.7817, -0.2334],
[-0.2822, 1.3192, -1.3645], [0.1533, 1.5024, -1.0432],
[0.4917, 1.1529, -1.3496]],
[[-2.0289, 2.4952,
-0.1708], [-0.7188, 0.9956, -0.5096],
[-2.0668, 6.0278, -0.4875], [-1.9304, 3.3092, 0.6610],
[0.0949, 1.4332, 0.3140], [-1.2879, 2.0008, -0.7791],
[-0.7252, 0.9611, -0.6371], [0.4066, 1.4211, -0.2947],
[0.3220, 1.4447, 0.3548], [-0.9744, 2.3856,
-1.2000]]]).cuda()
idx = ball_query(0.2, 5, xyz, new_xyz)
expected_idx = torch.tensor([[[0, 0, 0, 0, 0], [6, 6, 6, 6, 6],
[2, 2, 2, 2, 2], [0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]],
[[0, 0, 0, 0, 0], [2, 2, 2, 2, 2],
[7, 7, 7, 7, 7], [0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]]]).cuda()
assert torch.all(idx == expected_idx)
def test_grouping_points():
if not torch.cuda.is_available():
pytest.skip()
idx = torch.tensor([[[0, 0, 0], [3, 3, 3], [8, 8, 8], [0, 0, 0], [0, 0, 0],
[0, 0, 0]],
[[0, 0, 0], [6, 6, 6], [9, 9, 9], [0, 0, 0], [0, 0, 0],
[0, 0, 0]]]).int().cuda()
festures = torch.tensor([[[
0.5798, -0.7981, -0.9280, -1.3311, 1.3687, 0.9277, -0.4164, -1.8274,
0.9268, 0.8414
],
[
5.4247, 1.5113, 2.3944, 1.4740, 5.0300,
5.1030, 1.9360, 2.1939, 2.1581, 3.4666
],
[
-1.6266, -1.0281, -1.0393, -1.6931, -1.3982,
-0.5732, -1.0830, -1.7561, -1.6786, -1.6967
]],
[[
-0.0380, -0.1880, -1.5724, 0.6905, -0.3190,
0.7798, -0.3693, -0.9457, -0.2942, -1.8527
],
[
1.1773, 1.5009, 2.6399, 5.9242, 1.0962,
2.7346, 6.0865, 1.5555, 4.3303, 2.8229
],
[
-0.6646, -0.6870, -0.1125, -0.2224, -0.3445,
-1.4049, 0.4990, -0.7037, -0.9924, 0.0386
]]]).cuda()
output = grouping_operation(festures, idx)
expected_output = torch.tensor([[[[0.5798, 0.5798, 0.5798],
[-1.3311, -1.3311, -1.3311],
[0.9268, 0.9268, 0.9268],
[0.5798, 0.5798, 0.5798],
[0.5798, 0.5798, 0.5798],
[0.5798, 0.5798, 0.5798]],
[[5.4247, 5.4247, 5.4247],
[1.4740, 1.4740, 1.4740],
[2.1581, 2.1581, 2.1581],
[5.4247, 5.4247, 5.4247],
[5.4247, 5.4247, 5.4247],
[5.4247, 5.4247, 5.4247]],
[[-1.6266, -1.6266, -1.6266],
[-1.6931, -1.6931, -1.6931],
[-1.6786, -1.6786, -1.6786],
[-1.6266, -1.6266, -1.6266],
[-1.6266, -1.6266, -1.6266],
[-1.6266, -1.6266, -1.6266]]],
[[[-0.0380, -0.0380, -0.0380],
[-0.3693, -0.3693, -0.3693],
[-1.8527, -1.8527, -1.8527],
[-0.0380, -0.0380, -0.0380],
[-0.0380, -0.0380, -0.0380],
[-0.0380, -0.0380, -0.0380]],
[[1.1773, 1.1773, 1.1773],
[6.0865, 6.0865, 6.0865],
[2.8229, 2.8229, 2.8229],
[1.1773, 1.1773, 1.1773],
[1.1773, 1.1773, 1.1773],
[1.1773, 1.1773, 1.1773]],
[[-0.6646, -0.6646, -0.6646],
[0.4990, 0.4990, 0.4990],
[0.0386, 0.0386, 0.0386],
[-0.6646, -0.6646, -0.6646],
[-0.6646, -0.6646, -0.6646],
[-0.6646, -0.6646, -0.6646]]]]).cuda()
assert torch.allclose(output, expected_output)
def test_gather_points():
if not torch.cuda.is_available():
pytest.skip()
features = torch.tensor([[[
-1.6095, -0.1029, -0.8876, -1.2447, -2.4031, 0.3708, -1.1586, -1.4967,
-0.4800, 0.2252
],
[
1.9138, 3.4979, 1.6854, 1.5631, 3.6776,
3.1154, 2.1705, 2.5221, 2.0411, 3.1446
],
[
-1.4173, 0.3073, -1.4339, -1.4340, -1.2770,
-0.2867, -1.4162, -1.4044, -1.4245, -1.4074
]],
[[
0.2160, 0.0842, 0.3661, -0.2749, -0.4909,
-0.6066, -0.8773, -0.0745, -0.9496, 0.1434
],
[
1.3644, 1.8087, 1.6855, 1.9563, 1.2746,
1.9662, 0.9566, 1.8778, 1.1437, 1.3639
],
[
-0.7172, 0.1692, 0.2241, 0.0721, -0.7540,
0.0462, -0.6227, 0.3223, -0.6944, -0.5294
]]]).cuda()
idx = torch.tensor([[0, 1, 4, 0, 0, 0], [0, 5, 6, 0, 0, 0]]).int().cuda()
output = gather_points(features, idx)
expected_output = torch.tensor(
[[[-1.6095, -0.1029, -2.4031, -1.6095, -1.6095, -1.6095],
[1.9138, 3.4979, 3.6776, 1.9138, 1.9138, 1.9138],
[-1.4173, 0.3073, -1.2770, -1.4173, -1.4173, -1.4173]],
[[0.2160, -0.6066, -0.8773, 0.2160, 0.2160, 0.2160],
[1.3644, 1.9662, 0.9566, 1.3644, 1.3644, 1.3644],
[-0.7172, 0.0462, -0.6227, -0.7172, -0.7172, -0.7172]]]).cuda()
assert torch.allclose(output, expected_output)
def test_three_interpolate():
if not torch.cuda.is_available():
pytest.skip()
features = torch.tensor([[[2.4350, 4.7516, 4.4995, 2.4350, 2.4350, 2.4350],
[3.1236, 2.6278, 3.0447, 3.1236, 3.1236, 3.1236],
[2.6732, 2.8677, 2.6436, 2.6732, 2.6732, 2.6732],
[0.0124, 7.0150, 7.0199, 0.0124, 0.0124, 0.0124],
[0.3207, 0.0000, 0.3411, 0.3207, 0.3207,
0.3207]],
[[0.0000, 0.9544, 2.4532, 0.0000, 0.0000, 0.0000],
[0.5346, 1.9176, 1.4715, 0.5346, 0.5346, 0.5346],
[0.0000, 0.2744, 2.0842, 0.0000, 0.0000, 0.0000],
[0.3414, 1.5063, 1.6209, 0.3414, 0.3414, 0.3414],
[0.5814, 0.0103, 0.0000, 0.5814, 0.5814,
0.5814]]]).cuda()
idx = torch.tensor([[[0, 1, 2], [2, 3, 4], [2, 3, 4], [0, 1, 2], [0, 1, 2],
[0, 1, 3]],
[[0, 2, 3], [1, 3, 4], [2, 1, 4], [0, 2, 4], [0, 2, 4],
[0, 1, 2]]]).int().cuda()
weight = torch.tensor([[[3.3333e-01, 3.3333e-01, 3.3333e-01],
[1.0000e+00, 5.8155e-08, 2.2373e-08],
[1.0000e+00, 1.7737e-08, 1.7356e-08],
[3.3333e-01, 3.3333e-01, 3.3333e-01],
[3.3333e-01, 3.3333e-01, 3.3333e-01],
[3.3333e-01, 3.3333e-01, 3.3333e-01]],
[[3.3333e-01, 3.3333e-01, 3.3333e-01],
[1.0000e+00, 1.3651e-08, 7.7312e-09],
[1.0000e+00, 1.7148e-08, 1.4070e-08],
[3.3333e-01, 3.3333e-01, 3.3333e-01],
[3.3333e-01, 3.3333e-01, 3.3333e-01],
[3.3333e-01, 3.3333e-01, 3.3333e-01]]]).cuda()
output = three_interpolate(features, idx, weight)
expected_output = torch.tensor([[[
3.8953e+00, 4.4995e+00, 4.4995e+00, 3.8953e+00, 3.8953e+00, 3.2072e+00
], [
2.9320e+00, 3.0447e+00, 3.0447e+00, 2.9320e+00, 2.9320e+00, 2.9583e+00
], [
2.7281e+00, 2.6436e+00, 2.6436e+00, 2.7281e+00, 2.7281e+00, 2.7380e+00
], [
4.6824e+00, 7.0199e+00, 7.0199e+00, 4.6824e+00, 4.6824e+00, 2.3466e+00
], [
2.2060e-01, 3.4110e-01, 3.4110e-01, 2.2060e-01, 2.2060e-01, 2.1380e-01
]],
[[
8.1773e-01, 9.5440e-01, 2.4532e+00,
8.1773e-01, 8.1773e-01, 1.1359e+00
],
[
8.4689e-01, 1.9176e+00, 1.4715e+00,
8.4689e-01, 8.4689e-01, 1.3079e+00
],
[
6.9473e-01, 2.7440e-01, 2.0842e+00,
6.9473e-01, 6.9473e-01, 7.8619e-01
],
[
7.6789e-01, 1.5063e+00, 1.6209e+00,
7.6789e-01, 7.6789e-01, 1.1562e+00
],
[
3.8760e-01, 1.0300e-02, 8.3569e-09,
3.8760e-01, 3.8760e-01, 1.9723e-01
]]]).cuda()
assert torch.allclose(output, expected_output, 1e-4)
def test_three_nn():
known = torch.tensor([[[-1.8373, 3.5605,
-0.7867], [0.7615, 2.9420, 0.2314],
[-0.6503, 3.6637, -1.0622],
[-1.8373, 3.5605, -0.7867],
[-1.8373, 3.5605, -0.7867]],
[[-1.3399, 1.9991, -0.3698],
[-0.0799, 0.9698,
-0.8457], [0.0858, 2.4721, -0.1928],
[-1.3399, 1.9991, -0.3698],
[-1.3399, 1.9991, -0.3698]]]).cuda()
unknown = torch.tensor([[[-1.8373, 3.5605, -0.7867],
[0.7615, 2.9420, 0.2314],
[-0.6503, 3.6637, -1.0622],
[-1.5237, 2.3976, -0.8097],
[-0.0722, 3.4017, -0.2880],
[0.5198, 3.0661, -0.4605],
[-2.0185, 3.5019, -0.3236],
[0.5098, 3.1020, 0.5799],
[-1.6137, 3.8443, -0.5269],
[0.7341, 2.9626, -0.3189]],
[[-1.3399, 1.9991, -0.3698],
[-0.0799, 0.9698, -0.8457],
[0.0858, 2.4721, -0.1928],
[-0.9022, 1.6560, -1.3090],
[0.1156, 1.6901, -0.4366],
[-0.6477, 2.3576, -0.1563],
[-0.8482, 1.1466, -1.2704],
[-0.8753, 2.0845, -0.3460],
[-0.5621, 1.4233, -1.2858],
[-0.5883, 1.3114, -1.2899]]]).cuda()
dist, idx = three_nn(unknown, known)
expected_dist = torch.tensor([[[0.0000, 0.0000, 0.0000],
[0.0000, 2.0463, 2.8588],
[0.0000, 1.2229, 1.2229],
[1.2047, 1.2047, 1.2047],
[1.0011, 1.0845, 1.8411],
[0.7433, 1.4451, 2.4304],
[0.5007, 0.5007, 0.5007],
[0.4587, 2.0875, 2.7544],
[0.4450, 0.4450, 0.4450],
[0.5514, 1.7206, 2.6811]],
[[0.0000, 0.0000, 0.0000],
[0.0000, 1.6464, 1.6952],
[0.0000, 1.5125, 1.5125],
[1.0915, 1.0915, 1.0915],
[0.8197, 0.8511, 1.4894],
[0.7433, 0.8082, 0.8082],
[0.8955, 1.3340, 1.3340],
[0.4730, 0.4730, 0.4730],
[0.7949, 1.3325, 1.3325],
[0.7566, 1.3727, 1.3727]]]).cuda()
expected_idx = torch.tensor([[[0, 3, 4], [1, 2, 0], [2, 0, 3], [0, 3, 4],
[2, 1, 0], [1, 2, 0], [0, 3, 4], [1, 2, 0],
[0, 3, 4], [1, 2, 0]],
[[0, 3, 4], [1, 2, 0], [2, 0, 3], [0, 3, 4],
[2, 1, 0], [2, 0, 3], [1, 0, 3], [0, 3, 4],
[1, 0, 3], [1, 0, 3]]]).cuda()
assert torch.allclose(dist, expected_dist, 1e-4)
assert torch.all(idx == expected_idx)
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